Lamarckian clonal selection algorithm based function optimization

Authors:
Wuhong He;Haifeng Du;Licheng Jiao;Jing Li
Affiliations:
Institute of Intelligent Information Processing and National Key Lab of Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab of Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab of Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab of Radar Signal Processing, Xidian University, Xi'an, China
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of “Gain and Convey” is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.